GEOSPATIAL MODELLING OF AIR POLLUTION AND ITS IMPACT ON HEALTH OF URBAN RESIDENTS USING SPATIAL MODELS: A REVIEW

Abstract

Air quality is a very important factor in projecting or representing the status of environment and health of any region particularly urban areas. Air pollution studies analysing the quality of air deliver strategic information to the decision-making process and play a significant role in the implementation of the policies that influence the air quality of a region. Majority of the air pollution models that simulate the distribution of pollutants consider various physical and environmental characteristics that include wind direction, speed, temperature, etc., which help in determination of the air pollution trajectory. The integration of these models in GIS gives a geophysical dimension to the air quality information by relating the actual pollution concentrations to the health of plant and human life in that location. Over the recent years, several efforts have been made to map traffic-related emission and determine pollution patterns in urban areas using GIS. The use of GIS as a tool to illustrate the spatial patterns of emission and to visualize the impact of congestion on human health has long been attempted. To simulate the impact of air quality in terms of transportation and land use policy changes, several integrated models can be performed. GIS is a dynamic tool when combined with statistical analysis to map traffic-related air pollution and to generate predictive models of pollution surfaces. These models are useful to develop decisions based on monitored pollution data and exogenous information. With this background, a review on GIS-based methods to evaluate the impact of air pollution on human health has been presented. The complexity of using GIS for integrated air quality mapping and its impact on human health lies in many domains. Understanding the relationships between health, environment, geology, hydrology, air pollution studies, agronomy and their dependencies in a spatial phenomenon is the major crux. Spatial explorative models that can determine the relationships between the environment and high pollution concentrations across various demographic layers can help in identifying hotspots that demand special investigation or monitoring. Data visualization which otherwise means illustrating complex information through a map provides a dynamic insight to help the authorities plan future strategies.